สร้างวิดีโอด้วย Veo 3.1 ใน Gemini API

import time
from google import genai
from google.genai import types

client = genai.Client()

prompt = """A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.
A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'"""

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the generated video.
generated_video = operation.response.generated_videos[0]
client.files.download(file=generated_video.video)
generated_video.video.save("dialogue_example.mp4")
print("Generated video saved to dialogue_example.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = `A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.
A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'`;

let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    prompt: prompt,
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the generated video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "dialogue_example.mp4",
});
console.log(`Generated video saved to dialogue_example.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    prompt := `A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.
    A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'`

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
        nil,
        nil,
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
    log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the generated video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "dialogue_example.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

import com.google.genai.Client;
import com.google.genai.types.GenerateVideosOperation;
import com.google.genai.types.Video;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

class GenerateVideoFromText {
  public static void main(String[] args) throws Exception {
    Client client = new Client();

    String prompt = "A close up of two people staring at a cryptic drawing on a wall, torchlight flickering.\n" +
"A man murmurs, 'This must be it. That's the secret code.' The woman looks at him and whispering excitedly, 'What did you find?'";

    GenerateVideosOperation operation =
        client.models.generateVideos("veo-3.1-generate-preview", prompt, null, null);

    // Poll the operation status until the video is ready.
    while (!operation.done().isPresent() || !operation.done().get()) {
      System.out.println("Waiting for video generation to complete...");
      Thread.sleep(10000);
      operation = client.operations.getVideosOperation(operation, null);
    }

    // Download the generated video.
    Video video = operation.response().get().generatedVideos().get().get(0).video().get();
    Path path = Paths.get("dialogue_example.mp4");
    client.files.download(video, path.toString(), null);
    if (video.videoBytes().isPresent()) {
      Files.write(path, video.videoBytes().get());
      System.out.println("Generated video saved to dialogue_example.mp4");
    }
  }
}

# Note: This script uses jq to parse the JSON response.
# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
        "prompt": "A close up of two people staring at a cryptic drawing on a wall, torchlight flickering. A man murmurs, \"This must be it. That'\''s the secret code.\" The woman looks at him and whispering excitedly, \"What did you find?\""
      }
    ]
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o dialogue_example.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 5 seconds before checking again.
  sleep 10
done

import time
from google import genai
from google.genai import types

client = genai.Client()

prompt = """A montage of pizza making: a chef tossing and flattening the floury dough, ladling rich red tomato sauce in a spiral, sprinkling mozzarella cheese and pepperoni, and a final shot of the bubbling golden-brown pizza, upbeat electronic music with a rhythmical beat is playing, high energy professional video."""

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
    config=types.GenerateVideosConfig(
      aspect_ratio="9:16",
    ),
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the generated video.
generated_video = operation.response.generated_videos[0]
client.files.download(file=generated_video.video)
generated_video.video.save("pizza_making.mp4")
print("Generated video saved to pizza_making.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = `A montage of pizza making: a chef tossing and flattening the floury dough, ladling rich red tomato sauce in a spiral, sprinkling mozzarella cheese and pepperoni, and a final shot of the bubbling golden-brown pizza, upbeat electronic music with a rhythmical beat is playing, high energy professional video.`;

let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    prompt: prompt,
    config: {
      aspectRatio: "9:16",
    },
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the generated video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "pizza_making.mp4",
});
console.log(`Generated video saved to pizza_making.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    prompt := `A montage of pizza making: a chef tossing and flattening the floury dough, ladling rich red tomato sauce in a spiral, sprinkling mozzarella cheese and pepperoni, and a final shot of the bubbling golden-brown pizza, upbeat electronic music with a rhythmical beat is playing, high energy professional video.`

  videoConfig := &genai.GenerateVideosConfig{
      AspectRatio: "9:16",
  }

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
        nil,
        videoConfig,
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
    log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the generated video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "pizza_making.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

# Note: This script uses jq to parse the JSON response.
# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
        "prompt": "A montage of pizza making: a chef tossing and flattening the floury dough, ladling rich red tomato sauce in a spiral, sprinkling mozzarella cheese and pepperoni, and a final shot of the bubbling golden-brown pizza, upbeat electronic music with a rhythmical beat is playing, high energy professional video."
      }
    ],
    "parameters": {
      "aspectRatio": "9:16"
    }
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o pizza_making.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 5 seconds before checking again.
  sleep 10
done

import time
from google import genai
from google.genai import types

client = genai.Client()

prompt = """A stunning drone view of the Grand Canyon during a flamboyant sunset that highlights the canyon's colors. The drone slowly flies towards the sun then accelerates, dives and flies inside the canyon."""

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
    config=types.GenerateVideosConfig(
      resolution="4k",
    ),
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the generated video.
generated_video = operation.response.generated_videos[0]
client.files.download(file=generated_video.video)
generated_video.video.save("4k_grand_canyon.mp4")
print("Generated video saved to 4k_grand_canyon.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = `A stunning drone view of the Grand Canyon during a flamboyant sunset that highlights the canyon's colors. The drone slowly flies towards the sun then accelerates, dives and flies inside the canyon.`;

let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    prompt: prompt,
    config: {
      resolution: "4k",
    },
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the generated video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "4k_grand_canyon.mp4",
});
console.log(`Generated video saved to 4k_grand_canyon.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    prompt := `A stunning drone view of the Grand Canyon during a flamboyant sunset that highlights the canyon's colors. The drone slowly flies towards the sun then accelerates, dives and flies inside the canyon.`

  videoConfig := &genai.GenerateVideosConfig{
      Resolution: "4k",
  }

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
        nil,
        videoConfig,
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
    log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the generated video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "4k_grand_canyon.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

# Note: This script uses jq to parse the JSON response.
# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
        "prompt": "A stunning drone view of the Grand Canyon during a flamboyant sunset that highlights the canyon'\''s colors. The drone slowly flies towards the sun then accelerates, dives and flies inside the canyon."
      }
    ],
    "parameters": {
      "resolution": "4k"
    }
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o 4k_grand_canyon.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 5 seconds before checking again.
  sleep 10
done

import time
from google import genai

client = genai.Client()

prompt = "Panning wide shot of a calico kitten sleeping in the sunshine"

# Step 1: Generate an image with Nano Banana.
image = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=prompt,
    config={"response_modalities":['IMAGE']}
)

# Step 2: Generate video with Veo 3.1 using the image.
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
    image=image.parts[0].as_image(),
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the video.
video = operation.response.generated_videos[0]
client.files.download(file=video.video)
video.video.save("veo3_with_image_input.mp4")
print("Generated video saved to veo3_with_image_input.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = "Panning wide shot of a calico kitten sleeping in the sunshine";

// Step 1: Generate an image with Nano Banana.
const imageResponse = await ai.models.generateContent({
  model: "gemini-2.5-flash-image",
  prompt: prompt,
});

// Step 2: Generate video with Veo 3.1 using the image.
let operation = await ai.models.generateVideos({
  model: "veo-3.1-generate-preview",
  prompt: prompt,
  image: {
    imageBytes: imageResponse.generatedImages[0].image.imageBytes,
    mimeType: "image/png",
  },
});

// Poll the operation status until the video is ready.
while (!operation.done) {
  console.log("Waiting for video generation to complete...")
  await new Promise((resolve) => setTimeout(resolve, 10000));
  operation = await ai.operations.getVideosOperation({
    operation: operation,
  });
}

// Download the video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "veo3_with_image_input.mp4",
});
console.log(`Generated video saved to veo3_with_image_input.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    prompt := "Panning wide shot of a calico kitten sleeping in the sunshine"

    // Step 1: Generate an image with Nano Banana.
    imageResponse, err := client.Models.GenerateContent(
        ctx,
        "gemini-2.5-flash-image",
        prompt,
        nil, // GenerateImagesConfig
    )
    if err != nil {
        log.Fatal(err)
    }

    // Step 2: Generate video with Veo 3.1 using the image.
    operation, err := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
        imageResponse.GeneratedImages[0].Image,
        nil, // GenerateVideosConfig
    )
    if err != nil {
        log.Fatal(err)
    }

    // Poll the operation status until the video is ready.
    for !operation.Done {
        log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "veo3_with_image_input.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

import com.google.genai.Client;
import com.google.genai.types.GenerateVideosOperation;
import com.google.genai.types.Image;
import com.google.genai.types.Video;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

class GenerateVideoFromImage {
  public static void main(String[] args) throws Exception {
    Client client = new Client();

    String prompt = "Panning wide shot of a calico kitten sleeping in the sunshine";

    // Step 1: Generate an image with Nano Banana:
    // ...
    // We assume 'image' contains the generated image from step 1,
    // or is loaded from a file:
    Image image = Image.fromFile("path/to/your/image.png");

    // Step 2: Generate video with Veo 3.1 using the image.
    GenerateVideosOperation operation =
        client.models.generateVideos("veo-3.1-generate-preview", prompt, image, null);

    // Poll the operation status until the video is ready.
    while (!operation.done().isPresent() || !operation.done().get()) {
      System.out.println("Waiting for video generation to complete...");
      Thread.sleep(10000);
      operation = client.operations.getVideosOperation(operation, null);
    }

    // Download the video.
    Video video = operation.response().get().generatedVideos().get().get(0).video().get();
    Path path = Paths.get("veo3_with_image_input.mp4");
    client.files.download(video, path.toString(), null);
    if (video.videoBytes().isPresent()) {
      Files.write(path, video.videoBytes().get());
      System.out.println("Generated video saved to veo3_with_image_input.mp4");
    }
  }
}

import time
from google import genai

client = genai.Client()

prompt = "The video opens with a medium, eye-level shot of a beautiful woman with dark hair and warm brown eyes. She wears a magnificent, high-fashion flamingo dress with layers of pink and fuchsia feathers, complemented by whimsical pink, heart-shaped sunglasses. She walks with serene confidence through the crystal-clear, shallow turquoise water of a sun-drenched lagoon. The camera slowly pulls back to a medium-wide shot, revealing the breathtaking scene as the dress's long train glides and floats gracefully on the water's surface behind her. The cinematic, dreamlike atmosphere is enhanced by the vibrant colors of the dress against the serene, minimalist landscape, capturing a moment of pure elegance and high-fashion fantasy."

dress_reference = types.VideoGenerationReferenceImage(
  image=dress_image, # Generated separately with Nano Banana
  reference_type="asset"
)

sunglasses_reference = types.VideoGenerationReferenceImage(
  image=glasses_image, # Generated separately with Nano Banana
  reference_type="asset"
)

woman_reference = types.VideoGenerationReferenceImage(
  image=woman_image, # Generated separately with Nano Banana
  reference_type="asset"
)

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
    config=types.GenerateVideosConfig(
      reference_images=[dress_reference, glasses_reference, woman_reference],
    ),
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the video.
video = operation.response.generated_videos[0]
client.files.download(file=video.video)
video.video.save("veo3.1_with_reference_images.mp4")
print("Generated video saved to veo3.1_with_reference_images.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = "The video opens with a medium, eye-level shot of a beautiful woman with dark hair and warm brown eyes. She wears a magnificent, high-fashion flamingo dress with layers of pink and fuchsia feathers, complemented by whimsical pink, heart-shaped sunglasses. She walks with serene confidence through the crystal-clear, shallow turquoise water of a sun-drenched lagoon. The camera slowly pulls back to a medium-wide shot, revealing the breathtaking scene as the dress's long train glides and floats gracefully on the water's surface behind her. The cinematic, dreamlike atmosphere is enhanced by the vibrant colors of the dress against the serene, minimalist landscape, capturing a moment of pure elegance and high-fashion fantasy.";

// dressImage, glassesImage, womanImage generated separately with Nano Banana
// and available as objects like { imageBytes: "...", mimeType: "image/png" }
const dressReference = {
  image: dressImage,
  referenceType: "asset",
};
const sunglassesReference = {
  image: glassesImage,
  referenceType: "asset",
};
const womanReference = {
  image: womanImage,
  referenceType: "asset",
};

let operation = await ai.models.generateVideos({
  model: "veo-3.1-generate-preview",
  prompt: prompt,
  config: {
    referenceImages: [
      dressReference,
      sunglassesReference,
      womanReference,
    ],
  },
});

// Poll the operation status until the video is ready.
while (!operation.done) {
  console.log("Waiting for video generation to complete...");
  await new Promise((resolve) => setTimeout(resolve, 10000));
  operation = await ai.operations.getVideosOperation({
    operation: operation,
  });
}

// Download the video.
ai.files.download({
  file: operation.response.generatedVideos[0].video,
  downloadPath: "veo3.1_with_reference_images.mp4",
});
console.log(`Generated video saved to veo3.1_with_reference_images.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

  prompt := `The video opens with a medium, eye-level shot of a beautiful woman with dark hair and warm brown eyes. She wears a magnificent, high-fashion flamingo dress with layers of pink and fuchsia feathers, complemented by whimsical pink, heart-shaped sunglasses. She walks with serene confidence through the crystal-clear, shallow turquoise water of a sun-drenched lagoon. The camera slowly pulls back to a medium-wide shot, revealing the breathtaking scene as the dress's long train glides and floats gracefully on the water's surface behind her. The cinematic, dreamlike atmosphere is enhanced by the vibrant colors of the dress against the serene, minimalist landscape, capturing a moment of pure elegance and high-fashion fantasy.`

  // dressImage, glassesImage, womanImage generated separately with Nano Banana
  // and available as *genai.Image objects.
  var dressImage, glassesImage, womanImage *genai.Image

  dressReference := &genai.VideoGenerationReferenceImage{
    Image: dressImage,
    ReferenceType: "asset",
  }
  sunglassesReference := &genai.VideoGenerationReferenceImage{
    Image: glassesImage,
    ReferenceType: "asset",
  }
  womanReference := &genai.VideoGenerationReferenceImage{
    Image: womanImage,
    ReferenceType: "asset",
  }

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
    nil, // image
        &genai.GenerateVideosConfig{
      ReferenceImages: []*genai.VideoGenerationReferenceImage{
        dressReference,
        sunglassesReference,
        womanReference,
      },
    },
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
        log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "veo3.1_with_reference_images.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

# Note: This script uses jq to parse the JSON response.
# It assumes dress_image_base64, glasses_image_base64, and woman_image_base64
# contain base64-encoded image data.

# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
      "prompt": "The video opens with a medium, eye-level shot of a beautiful woman with dark hair and warm brown eyes. She wears a magnificent, high-fashion flamingo dress with layers of pink and fuchsia feathers, complemented by whimsical pink, heart-shaped sunglasses. She walks with serene confidence through the crystal-clear, shallow turquoise water of a sun-drenched lagoon. The camera slowly pulls back to a medium-wide shot, revealing the breathtaking scene as the dress'\''s long train glides and floats gracefully on the water'\''s surface behind her. The cinematic, dreamlike atmosphere is enhanced by the vibrant colors of the dress against the serene, minimalist landscape, capturing a moment of pure elegance and high-fashion fantasy.",
      "referenceImages": [
        {
          "image": {"inlineData": {"mimeType": "image/png", "data": "'"$dress_image_base64"'"}},
          "referenceType": "asset"
        },
        {
          "image": {"inlineData": {"mimeType": "image/png", "data": "'"$glasses_image_base64"'"}},
          "referenceType": "asset"
        },
        {
          "image": {"inlineData": {"mimeType": "image/png", "data": "'"$woman_image_base64"'"}},
          "referenceType": "asset"
        }
      ]
    }],
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o veo3.1_with_reference_images.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 10 seconds before checking again.
  sleep 10
done

import time
from google import genai

client = genai.Client()

prompt = "A cinematic, haunting video. A ghostly woman with long white hair and a flowing dress swings gently on a rope swing beneath a massive, gnarled tree in a foggy, moonlit clearing. The fog thickens and swirls around her, and she slowly fades away, vanishing completely. The empty swing is left swaying rhythmically on its own in the eerie silence."

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt=prompt,
    image=first_image, # The starting frame is passed as a primary input
    config=types.GenerateVideosConfig(
      last_frame=last_image # The ending frame is passed as a generation constraint in the config
    ),
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the video.
video = operation.response.generated_videos[0]
client.files.download(file=video.video)
video.video.save("veo3.1_with_interpolation.mp4")
print("Generated video saved to veo3.1_with_interpolation.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = "A cinematic, haunting video. A ghostly woman with long white hair and a flowing dress swings gently on a rope swing beneath a massive, gnarled tree in a foggy, moonlit clearing. The fog thickens and swirls around her, and she slowly fades away, vanishing completely. The empty swing is left swaying rhythmically on its own in the eerie silence.";

// firstImage and lastImage generated separately with Nano Banana
// and available as objects like { imageBytes: "...", mimeType: "image/png" }
let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    prompt: prompt,
    image: firstImage, // The starting frame is passed as a primary input
    config: {
      lastFrame: lastImage, // The ending frame is passed as a generation constraint in the config
    },
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "veo3.1_with_interpolation.mp4",
});
console.log(`Generated video saved to veo3.1_with_interpolation.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

  prompt := `A cinematic, haunting video. A ghostly woman with long white hair and a flowing dress swings gently on a rope swing beneath a massive, gnarled tree in a foggy, moonlit clearing. The fog thickens and swirls around her, and she slowly fades away, vanishing completely. The empty swing is left swaying rhythmically on its own in the eerie silence.`

  // firstImage and lastImage generated separately with Nano Banana
  // and available as *genai.Image objects.
  var firstImage, lastImage *genai.Image

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
    firstImage, // The starting frame is passed as a primary input
        &genai.GenerateVideosConfig{
      LastFrame: lastImage, // The ending frame is passed as a generation constraint in the config
    },
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
        log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "veo3.1_with_interpolation.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

# Note: This script uses jq to parse the JSON response.
# It assumes first_image_base64 and last_image_base64
# contain base64-encoded image data.

# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
# The starting frame is passed as a primary input
# The ending frame is passed as a generation constraint in the config
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
      "prompt": "A cinematic, haunting video. A ghostly woman with long white hair and a flowing dress swings gently on a rope swing beneath a massive, gnarled tree in a foggy, moonlit clearing. The fog thickens and swirls around her, and she slowly fades away, vanishing completely. The empty swing is left swaying rhythmically on its own in the eerie silence.",
      "image": {"inlineData": {"mimeType": "image/png", "data": "'"$first_image_base64"'"}},
      "lastFrame": {"inlineData": {"mimeType": "image/png", "data": "'"$last_image_base64"'"}}
    }],
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o veo3.1_with_interpolation.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 10 seconds before checking again.
  sleep 10
done

import time
from google import genai

client = genai.Client()

prompt = "Track the butterfly into the garden as it lands on an orange origami flower. A fluffy white puppy runs up and gently pats the flower."

operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    video=operation.response.generated_videos[0].video, # This must be a video from a previous generation
    prompt=prompt,
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        resolution="720p"
    ),
)

# Poll the operation status until the video is ready.
while not operation.done:
    print("Waiting for video generation to complete...")
    time.sleep(10)
    operation = client.operations.get(operation)

# Download the video.
video = operation.response.generated_videos[0]
client.files.download(file=video.video)
video.video.save("veo3.1_extension.mp4")
print("Generated video saved to veo3.1_extension.mp4")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const prompt = "Track the butterfly into the garden as it lands on an orange origami flower. A fluffy white puppy runs up and gently pats the flower.";

// butterflyVideo must be a video from a previous generation
// available as an object like { videoBytes: "...", mimeType: "video/mp4" }
let operation = await ai.models.generateVideos({
    model: "veo-3.1-generate-preview",
    video: butterflyVideo,
    prompt: prompt,
    config: {
        numberOfVideos: 1,
        resolution: "720p",
    },
});

// Poll the operation status until the video is ready.
while (!operation.done) {
    console.log("Waiting for video generation to complete...")
    await new Promise((resolve) => setTimeout(resolve, 10000));
    operation = await ai.operations.getVideosOperation({
        operation: operation,
    });
}

// Download the video.
ai.files.download({
    file: operation.response.generatedVideos[0].video,
    downloadPath: "veo3.1_extension.mp4",
});
console.log(`Generated video saved to veo3.1_extension.mp4`);

package main

import (
    "context"
    "log"
    "os"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

  prompt := `Track the butterfly into the garden as it lands on an orange origami flower. A fluffy white puppy runs up and gently pats the flower.`

  // butterflyVideo must be a video from a previous generation
  // available as a *genai.Video object.
  var butterflyVideo *genai.Video

    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        prompt,
    nil, // image
    butterflyVideo,
        &genai.GenerateVideosConfig{
      NumberOfVideos: 1,
      Resolution: "720p",
    },
    )

    // Poll the operation status until the video is ready.
    for !operation.Done {
        log.Println("Waiting for video generation to complete...")
        time.Sleep(10 * time.Second)
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Download the video.
    video := operation.Response.GeneratedVideos[0]
    client.Files.Download(ctx, video.Video, nil)
    fname := "veo3.1_extension.mp4"
    _ = os.WriteFile(fname, video.Video.VideoBytes, 0644)
    log.Printf("Generated video saved to %s\n", fname)
}

# Note: This script uses jq to parse the JSON response.
# It assumes butterfly_video_base64 contains base64-encoded
# video data from a previous generation.

# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
      "prompt": "Track the butterfly into the garden as it lands on an orange origami flower. A fluffy white puppy runs up and gently pats the flower.",
      "video": {"inlineData": {"mimeType": "video/mp4", "data": "'"$butterfly_video_base64"'"}}
    }],
    "parameters": {
      "numberOfVideos": 1,
      "resolution": "720p"
    }
  }' | jq -r .name)

# Poll the operation status until the video is ready
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Extract the download URI from the final response.
    video_uri=$(echo "${status_response}" | jq -r '.response.generateVideoResponse.generatedSamples[0].video.uri')
    echo "Downloading video from: ${video_uri}"

    # Download the video using the URI and API key and follow redirects.
    curl -L -o veo3.1_extension.mp4 -H "x-goog-api-key: $GEMINI_API_KEY" "${video_uri}"
    break
  fi
  # Wait for 10 seconds before checking again.
  sleep 10
done

import time
from google import genai
from google.genai import types

client = genai.Client()

# After starting the job, you get an operation object.
operation = client.models.generate_videos(
    model="veo-3.1-generate-preview",
    prompt="A cinematic shot of a majestic lion in the savannah.",
)

# Alternatively, you can use operation.name to get the operation.
operation = types.GenerateVideosOperation(name=operation.name)

# This loop checks the job status every 10 seconds.
while not operation.done:
    time.sleep(10)
    # Refresh the operation object to get the latest status.
    operation = client.operations.get(operation)

# Once done, the result is in operation.response.
# ... process and download your video ...

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

// After starting the job, you get an operation object.
let operation = await ai.models.generateVideos({
  model: "veo-3.1-generate-preview",
  prompt: "A cinematic shot of a majestic lion in the savannah.",
});

// Alternatively, you can use operation.name to get the operation.
// operation = types.GenerateVideosOperation(name=operation.name)

// This loop checks the job status every 10 seconds.
while (!operation.done) {
    await new Promise((resolve) => setTimeout(resolve, 1000));
    // Refresh the operation object to get the latest status.
    operation = await ai.operations.getVideosOperation({ operation });
}

// Once done, the result is in operation.response.
// ... process and download your video ...

package main

import (
    "context"
    "log"
    "time"

    "google.golang.org/genai"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    // After starting the job, you get an operation object.
    operation, _ := client.Models.GenerateVideos(
        ctx,
        "veo-3.1-generate-preview",
        "A cinematic shot of a majestic lion in the savannah.",
        nil,
        nil,
    )

    // This loop checks the job status every 10 seconds.
    for !operation.Done {
        time.Sleep(10 * time.Second)
        // Refresh the operation object to get the latest status.
        operation, _ = client.Operations.GetVideosOperation(ctx, operation, nil)
    }

    // Once done, the result is in operation.Response.
    // ... process and download your video ...
}

import com.google.genai.Client;
import com.google.genai.types.GenerateVideosOperation;
import com.google.genai.types.Video;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

class HandleAsync {
  public static void main(String[] args) throws Exception {
    Client client = new Client();

    // After starting the job, you get an operation object.
    GenerateVideosOperation operation =
        client.models.generateVideos(
            "veo-3.1-generate-preview",
            "A cinematic shot of a majestic lion in the savannah.",
            null,
            null);

    // This loop checks the job status every 10 seconds.
    while (!operation.done().isPresent() || !operation.done().get()) {
      Thread.sleep(10000);
      // Refresh the operation object to get the latest status.
      operation = client.operations.getVideosOperation(operation, null);
    }

    // Once done, the result is in operation.response.
    // Download the generated video.
    Video video = operation.response().get().generatedVideos().get().get(0).video().get();
    Path path = Paths.get("async_example.mp4");
    client.files.download(video, path.toString(), null);
    if (video.videoBytes().isPresent()) {
      Files.write(path, video.videoBytes().get());
      System.out.println("Generated video saved to async_example.mp4");
    }
  }
}

# Note: This script uses jq to parse the JSON response.
# GEMINI API Base URL
BASE_URL="https://generativelanguage.googleapis.com/v1beta"

# Send request to generate video and capture the operation name into a variable.
operation_name=$(curl -s "${BASE_URL}/models/veo-3.1-generate-preview:predictLongRunning" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X "POST" \
  -d '{
    "instances": [{
        "prompt": "A cinematic shot of a majestic lion in the savannah."
      }
    ]
  }' | jq -r .name)

# This loop checks the job status every 10 seconds.
while true; do
  # Get the full JSON status and store it in a variable.
  status_response=$(curl -s -H "x-goog-api-key: $GEMINI_API_KEY" "${BASE_URL}/${operation_name}")

  # Check the "done" field from the JSON stored in the variable.
  is_done=$(echo "${status_response}" | jq .done)

  if [ "${is_done}" = "true" ]; then
    # Once done, the result is in status_response.
    # ... process and download your video ...
    echo "Video generation complete."
    break
  fi
  # Wait for 10 seconds before checking again.
  echo "Waiting for video generation to complete..."
  sleep 10
done